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Purpose: Guidelines recommend measuring myocardial extracellular volume (ECV) using T1‐mapping before and 10–30 min after contrast agent administration. Data are then analyzed using a linear model (LM), which assumes fast water exchange (WX) between the ECV and cardiomyocytes. We investigated whether limited WX influences ECV measurements in patients with severe aortic stenosis (AS). Methods: Twenty‐five patients with severe AS and 5 healthy controls were recruited. T1 measurements were made on a 3 T Siemens system using a multiparametric saturation‐recovery single‐shot acquisition (a) before contrast; (b) 4 min post 0.05 mmol/kg gadobutrol; and (c) 4 min, (d) 10 min, and (e) 30 min after an additional gadobutrol dose (0.1 mmol/kg). Three LM‐based ECV estimates, made using paired T1 measurements (a and b), (a and d), and (a and e), were compared to ECV estimates made using all 5 T1 measurements and a two‐site exchange model (2SXM) accounting for WX. Results: Median (range) ECV estimated using the 2SXM model was 25% (21%–39%) for patients and 26% (22%–29%) for controls. ECV estimated in patients using the LM at 10 min following a cumulative contrast dose of 0.15 mmol/kg was 21% (17%–32%) and increased significantly to 22% (19%–35%) at 30 min (p = 0.0001). ECV estimated using the LM was highest following low dose gadobutrol, 25% (19%–38%). Conclusion: Current guidelines on contrast agent dose for ECV measurements may lead to underestimated ECV in patients with severe AS because of limited WX. Use of a lower contrast agent dose may mitigate this effect. [ABSTRACT FROM AUTHOR]

Background: Determination of myocardial blood flow (MBF) with MRI is usually performed with dynamic contrast enhanced imaging (MBFDCE). MBF can also be determined from coronary sinus blood flow (MBFCS), which has the advantage of being a noncontrast technique. However, comparative studies of MBFDCE and MBFCS in large cohorts are lacking. Purpose: To compare MBFCS and MBFDCE in a large cohort. Study Type: Prospective, sequence‐comparison study. Population: 147 patients with type 2 diabetes mellitus (age: 56+/−12 years; 106 male; diabetes duration: 12.9+/−8.1 years), and 25 age‐matched controls. Field Strength/Sequences: 1.5 Tesla scanner. Saturation recovery sequence for MBFDCE vs. phase‐contrast gradient‐echo pulse sequence (free‐breathing) for MBFCS. Assessment: MBFDCE and MBFCS were determined at rest and during coronary dilatation achieved by administration of adenosine at 140 μg/kg/min. Myocardial perfusion reserve (MPR) was calculated as the stress/rest ratio of MBF values. Coronary sinus flow was determined twice in the same imaging session for repeatability assessment. Statistical Tests: Agreement between MBFDCE and MBFCS was assessed with Bland and Altman's technique. Repeatability was determined from single‐rater random intraclass and repeatability coefficients. Results: Rest and stress flows, including both MBFDCE and MBFCS values, ranged from 33 to 146 mL/min/100 g and 92 to 501 mL/min/100 g, respectively. Intraclass and repeatability coefficients for MBFCS were 0.95 (CI 0.90; 0.95) and 5 mL/min/100 g. In Bland–Altman analysis, mean bias at rest was –1.1 mL/min/100 g (CI −3.1; 0.9) with limits of agreement of −27 and 24.8 mL/min/100 g. Mean bias at stress was 6.3 mL/min/100 g (CI −1.1; 14.1) with limits of agreement of −86.9 and 99.9. Mean bias of MPR was 0.11 (CI: −0.02; 0.23) with limits of agreement of −1.43 and 1.64. Conclusion: MBF may be determined from coronary sinus blood flow, with acceptable bias, but relatively large limits of agreement, against the reference of MBFDCE. Level of Evidence: 1 Technical Efficacy Stage: 2 [ABSTRACT FROM AUTHOR]

Purpose: DTI characterizes tissue microstructure and provides proxy measures of nerve health. Echo‐planar imaging is a popular method of acquiring DTI but is susceptible to various artifacts (e.g., susceptibility, motion, and eddy currents), which may be ameliorated via preprocessing. There are many pipelines available but limited data comparing their performance, which provides the rationale for this study. Methods: DTI was acquired from the upper limb of heathy volunteers at 3T in blip‐up and blip‐down directions. Data were independently corrected using (i) FSL's TOPUP & eddy, (ii) FSL's TOPUP, (iii) DSI Studio, and (iv) TORTOISE. DTI metrics were extracted from the median, radial, and ulnar nerves and compared (between pipelines) using mixed‐effects linear regression. The geometric similarity of corrected b = 0 images and the slice matched T1‐weighted (T1w) images were computed using the Sörenson‐Dice coefficient. Results: Without preprocessing, the similarity coefficient of the blip‐up and blip‐down datasets to the T1w was 0·80 and 0·79, respectively. Preprocessing improved the geometric similarity by 1% with no difference between pipelines. Compared to TOPUP & eddy, DSI Studio and TORTOISE generated 2% and 6% lower estimates of fractional anisotropy, and 6% and 13% higher estimates of radial diffusivity, respectively. Estimates of anisotropy from TOPUP & eddy versus TOPUP were not different but TOPUP reduced radial diffusivity by 3%. The agreement of DTI metrics between pipelines was poor. Conclusions: Preprocessing DTI from the upper limb improves geometric similarity but the choice of the pipeline introduces clinically important variability in diffusion parameter estimates from peripheral nerves. [ABSTRACT FROM AUTHOR]